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- Ali, Nauman bin, et al.
(författare)
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On the search for industry-relevant regression testing research
- 2019
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Ingår i: Empirical Software Engineering. - New York, NY : Springer. - 1382-3256 .- 1573-7616. ; 24:4, s. 2020-2055
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Tidskriftsartikel (refereegranskat)abstract
- Regression testing is a means to assure that a change in the software, or its execution environment, does not introduce new defects. It involves the expensive undertaking of rerunning test cases. Several techniques have been proposed to reduce the number of test cases to execute in regression testing, however, there is no research on how to assess industrial relevance and applicability of such techniques. We conducted a systematic literature review with the following two goals: firstly, to enable researchers to design and present regression testing research with a focus on industrial relevance and applicability and secondly, to facilitate the industrial adoption of such research by addressing the attributes of concern from the practitioners' perspective. Using a reference-based search approach, we identified 1068 papers on regression testing. We then reduced the scope to only include papers with explicit discussions about relevance and applicability (i.e. mainly studies involving industrial stakeholders). Uniquely in this literature review, practitioners were consulted at several steps to increase the likelihood of achieving our aim of identifying factors important for relevance and applicability. We have summarised the results of these consultations and an analysis of the literature in three taxonomies, which capture aspects of industrial-relevance regarding the regression testing techniques. Based on these taxonomies, we mapped 38 papers reporting the evaluation of 26 regression testing techniques in industrial settings. © The Author(s) 2019
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| 2. |
- Kunze, Sebastian, 1990-, et al.
(författare)
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Generation of Failure Models through Automata Learning
- 2016
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Ingår i: Proceedings. - Los Alamitos : IEEE Computer Society. - 9781509025718 ; , s. 22-25
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Konferensbidrag (refereegranskat)abstract
- In the context of the AUTO-CAAS project that deals with model-based testing techniques applied in the automotive domain, we present the preliminary ideas and results of building generalised failure models for non-conformant software components. These models are a necessary building block for our upcoming efforts to detect and analyse failure causes in automotive software built with AUTOSAR components. Concretely, we discuss how to build these generalised failure models using automata learning techniques applied to a guided model-based testing procedure of a failing component. We illustrate our preliminary findings and experiments on a simple integer queue implemented in the C programming language. © 2016 IEEE.
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